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        <a href="deepbelief-module.html">Package&nbsp;deepbelief</a> ::
        Module&nbsp;utils
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<!-- ==================== MODULE DESCRIPTION ==================== -->
<h1 class="epydoc">Module utils</h1><p class="nomargin-top"><span class="codelink"><a href="deepbelief.utils-pysrc.html">source&nbsp;code</a></span></p>
<hr />
<div class="fields">      <p><strong>Author:</strong>
        Lucas Theis &lt;lucas@tuebingen.mpg.de&gt;
      </p>
      <p><strong>License:</strong>
        MIT License 
        &lt;http://www.opensource.org/licenses/mit-license.php&gt;
      </p>
</div><!-- ==================== FUNCTIONS ==================== -->
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    <span class="table-header">Functions</span></td>
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    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">matrix</span>
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          <td><span class="summary-sig"><a href="deepbelief.utils-module.html#binary_numbers" class="summary-sig-name">binary_numbers</a>(<span class="summary-sig-arg">n</span>)</span><br />
      Generates an n x 2^n matrix whose columns are filled with 
      binary-encoded numbers.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="deepbelief.utils-pysrc.html#binary_numbers">source&nbsp;code</a></span>
            
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      <span class="summary-type">matrix</span>
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          <td><span class="summary-sig"><a href="deepbelief.utils-module.html#logsumexp" class="summary-sig-name">logsumexp</a>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">ax</span>=<span class="summary-sig-default">None</span>)</span><br />
      Computes the log of the sum of the exp of the entries in x in a 
      numerically stable way.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="deepbelief.utils-pysrc.html#logsumexp">source&nbsp;code</a></span>
            
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      <span class="summary-type">matrix</span>
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          <td><span class="summary-sig"><a href="deepbelief.utils-module.html#logmeanexp" class="summary-sig-name">logmeanexp</a>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">ax</span>=<span class="summary-sig-default">None</span>)</span><br />
      Computes the log of the mean of the exp of the entries in x in a 
      numerically stable way.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="deepbelief.utils-pysrc.html#logmeanexp">source&nbsp;code</a></span>
            
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<!-- ==================== VARIABLES ==================== -->
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        <a name="__package__"></a><span class="summary-name">__package__</span> = <code title="'deepbelief'"><code class="variable-quote">'</code><code class="variable-string">deepbelief</code><code class="variable-quote">'</code></code>
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<a name="binary_numbers"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">binary_numbers</span>(<span class="sig-arg">n</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="deepbelief.utils-pysrc.html#binary_numbers">source&nbsp;code</a></span>&nbsp;
    </td>
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  <p>Generates an n x 2^n matrix whose columns are filled with 
  binary-encoded numbers.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>n</code></strong> (integer) - number of bits</li>
    </ul></dd>
    <dt>Returns: matrix</dt>
        <dd>matrix filled with binary numbers</dd>
  </dl>
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</div>
<a name="logsumexp"></a>
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  <table width="100%" cellpadding="0" cellspacing="0" border="0">
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">logsumexp</span>(<span class="sig-arg">x</span>,
        <span class="sig-arg">ax</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="deepbelief.utils-pysrc.html#logsumexp">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Computes the log of the sum of the exp of the entries in x in a 
  numerically stable way.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>x</code></strong> (array_like) - a list or a matrix of numbers</li>
        <li><strong class="pname"><code>ax</code></strong> (integer) - axis along which the sum is applied</li>
    </ul></dd>
    <dt>Returns: matrix</dt>
        <dd>a matrix containing the results</dd>
  </dl>
</td></tr></table>
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<a name="logmeanexp"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">logmeanexp</span>(<span class="sig-arg">x</span>,
        <span class="sig-arg">ax</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="deepbelief.utils-pysrc.html#logmeanexp">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <p>Computes the log of the mean of the exp of the entries in x in a 
  numerically stable way. Uses logsumexp.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>x</code></strong> (array_like) - a list or a matrix of numbers</li>
        <li><strong class="pname"><code>ax</code></strong> (integer) - axis along which the values are averaged</li>
    </ul></dd>
    <dt>Returns: matrix</dt>
        <dd>a matrix containing the results</dd>
  </dl>
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